Current methods and systems for evidence-driven hypothesis analysis rely extensively on Bayesian inference systems. Bayesian inference systems measure how an initial probability in the truth or falsity of a hypothesis may change, when evidence relating to the hypothesis is observed. An example of a Bayesian inference system is Netica Bayesian Network Software by Norsys.
While Bayesian inference systems are useful in hypothesis analysis, it is difficult to prove the accuracy of Bayesian inference systems to measure the strength in the trueness (or veracity) of a hypothesis. Even if accuracy could be ascertained, Bayesian inference system can indicate that a hypothesis has a high chance of being true, and the hypothesis proves to be false. When the hypothesis is an event, Bayesian inference systems can indicate the event has a high chance of occurrence, and the event does not occur.
Current methods for evidence-driven hypothesis analysis involve analyzing competing hypotheses. When analyzing competing hypotheses each piece of evidence has some positive or negative contribution to whether each hypothesis is indicated or contraindicated. It can be desirable to analyze hypotheses that share some but not all same evidence. This can be referred to analysis of non-competing hypotheses.
Therefore, a system for analyzing competing and non-competing hypotheses that can provide a diagnostic strength of evidence and/or verify its veracity is desired.